🚀 Fix: Ensure backend starts and runs Flask app

app.py

@@ -7,7 +7,6 @@ import traceback
 
 app = Flask(__name__)
 
-# === Load Trained Model ===
 MODEL_PATH = "best_model.pkl"
 
 try:
@@ -26,32 +25,14 @@ def predict_single():
     try:
         data = request.get_json()
         df = pd.DataFrame([data])
+
         df["Store_Age"] = 2025 - df["Store_Establishment_Year"]
         df["Price_per_kg"] = df["Product_MRP"] / df["Product_Weight"]
         df["MRP_Band"] = pd.cut(df["Product_MRP"], bins=[0, 100, 200, float("inf")], labels=["Low", "Mid", "High"])
+
         pred_log = model.predict(df)[0]
         pred = np.expm1(pred_log)
         return jsonify({"Predicted_Sales": round(float(pred), 2)}), 200
-    except Exception as e:
-        return jsonify({"error": str(e)}), 500
-
-@app.route("/v1/forecastbatch", methods=["POST"])
-def predict_batch():
-    try:
-        file = request.files.get("file")
-        if file is None:
-            return jsonify({"error": "No file uploaded"}), 400
 
-        df = pd.read_csv(file)
-        df["Store_Age"] = 2025 - df["Store_Establishment_Year"]
-        df["Price_per_kg"] = df["Product_MRP"] / df["Product_Weight"]
-        df["MRP_Band"] = pd.cut(df["Product_MRP"], bins=[0, 100, 200, float("inf")], labels=["Low", "Mid", "High"])
-        preds = model.predict(df)
-        results = [round(float(np.expm1(p)), 2) for p in preds]
-        return jsonify({"Predictions": results}), 200
     except Exception as e:
         return jsonify({"error": str(e)}), 500
-
-if __name__ == "__main__":
-    port = int(os.environ.get("PORT", 7860))
-    app.run(host="0.0.0.0", port=port)